JOG

Ricevuto : Sono tutte innovazioni proposte a Team for the Planet attraverso il modulo di presentazione delle innovazioni

Restaurants know precisely when, what, and how much they'll order, produce and sell

La leva politica utilizzata
Sobrietà
Il settore commerciale
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Data di presentazione 11 luglio 2023 Fondatori Maya Hamadi Luogo di sviluppo Paris, France, Francia

Il progetto in dettaglio

NB: questo modulo deve essere compilato completamente dalle persone che propongono l'innovazione.

Qual è il problema risolto?

Traditional demand forecasting methods are inaccurate and time-consuming, restaurant managers spend hours poring over spreadsheets and manually adjusting their predictions. The food waste that results from this gut-instinct forecast represent 1.3B kg, 6% of global CO2 emissions (3x aviation)

Come si risolve?

A SaaS for restaurants that uses the power of AI and Machine Learning to analyze POS data and exogenous factors, creating precise forecasts for sales, demand, production, food orders quantities, and staff scheduling, reducing food waste and labor shortage

Chi sono i potenziali clienti?

Global Restaurant chains (fast food or full service chains) and Mass catering : $8B global market. The buyers and users are CEO, COO, owners, franchisee. Users are restaurant managers

In che modo questa soluzione è diversa?

Forecast not as a feature but the heart of the business. Fully integrated, no switch costs. Created by restaurateurs, problem lived by the founder and shared by the clients. End-to-End Operational Optimization, impacting the waste and optimizing the labor